高动态下基于AUKF的载波跟踪算法  

Carrier Tracking Algorithm Based on Adaptive Unscented Kalman Filter in High Dynamic

作　　者：冯琼华[1] 吴铁军[1,2] 马龙华[1] 

机构地区：[1]浙江大学导航制导与控制研究所,杭州310027 [2]浙江大学控制科学与工程学系,杭州310027

出　　处：《计算机工程》2012年第16期237-240,244,共5页Computer Engineering

基　　金：国家自然科学基金资助项目(61070003);浙江省自然科学基金资助项目(R1090052)

摘　　要：当载体处于高动态运动状态时,GPS接收机载波跟踪信号极易受到外部环境不确定因素的影响。若采用标准的无迹卡尔曼滤波(UKF),在先验的噪声统计特性与实际的噪声统计特性不相符时,状态估计性能将变差甚至发散。针对上述问题,提出采用主从式自适应UKF的算法(AUKF)。AUKF能自适应调整过程噪声方差,从而达到减小模型估计误差、抑制滤波发散的目的。Matlab仿真结果表明,在高动态下噪声统计特性发生变化时,基于AUKF的载波跟踪算法具有较好的稳定性。The carrier tracking signal of the GPS receiver is vulnerable to environmental uncertainties when a receiver system is in high dynamic. The state estimation performance of the standard Unscented Kalman Filter（UKF） becomes poor and even divergent when the prior knowledge about the noise statistical characteristics does not meet real situations. To deal with this problem, a master-slaver Adaptive UKF（AUKF） algorithm is proposed. AUKF can adjust the noise eovariance, so as to minimize the estimation error of the model, and inhibit the divergence of filtering. Matlab simulation results show that the carrier tracking algorithm based on AUKF has good stability when the noise statistical characteristics changes in the high dynamic.

关 键 词：高动态 GPS接收机 噪声统计特性 载波跟踪 无迹卡尔曼滤波 自适应UKF 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]